用智能手表信号模拟鞋上传感器数据，实现日常步态分析
Conditional Generative Adversarial Networks Based Inertial Signal Translation
- 用条件GAN将腕戴传感器信号转为鞋载信号
- 转换后步态分析准确率显著提升，适配主流算法
- 支持日常使用，无需更换穿戴设备
本文提出一种基于条件生成对抗网络（Conditional GANs）的惯性信号转换方法，将腕戴式传感器（如智能手表）采集的惯性信号转换为鞋载传感器记录的信号，从而可直接应用先进的步态分析方法。实验采用两种GAN结构：基于二元交叉熵损失的传统GAN与基于Wasserstein距离的WGAN。生成器分别测试了卷积自编码器与卷积U-Net两种架构。结果表明，该方法能实现高精度信号转换，使腕戴传感器数据可用于高效、日常化的步态分析。
原文摘要 · Abstract (English)
The paper presents an approach in which inertial signals measured with a wrist-worn sensor (e.g., a smartwatch) are translated into those that would be recorded using a shoe-mounted sensor, enabling the use of state-of-the-art gait analysis methods. In the study, the signals are translated using Conditional Generative Adversarial Networks (GANs). Two different GAN versions are used for experimental verification: traditional ones trained using binary cross-entropy loss and Wasserstein GANs (WGANs). For the generator, two architectures, a convolutional autoencoder, and a convolutional U-Net, are tested. The experiment results have shown that the proposed approach allows for an accurate translation, enabling the use of wrist sensor inertial signals for efficient, every-day gait analysis.
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